Beijing, China

Binbin Xu

USPTO Granted Patents = 6 

Average Co-Inventor Count = 6.7

ph-index = 1


Company Filing History:


Years Active: 2024-2025

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6 patents (USPTO):

Title: Binbin Xu: Innovator in Deep Learning Technology

Introduction

Binbin Xu is a prominent inventor based in Beijing, China. He has made significant contributions to the fields of computer technology and deep learning. With a total of six patents to his name, Xu is recognized for his innovative approaches to resource scheduling and deep learning model adaptation.

Latest Patents

Xu's latest patents include a resource scheduling method, device, and storage medium. This invention relates to optimizing resource scheduling in computer technology, particularly in deep learning. The method involves acquiring a graphics processing unit (GPU) topology relationship of a cluster and determining the target computing node and GPU for task requests. This innovation aims to enhance the efficiency of GPU resource scheduling.

Another notable patent is a method and apparatus for adapting deep learning models. This invention addresses the challenges of applying deep learning models across different hardware platforms. By utilizing model and hardware information, Xu's method allows for the conversion of any original deep learning model into a target model suitable for various hardware, thereby facilitating broader application of deep learning technologies.

Career Highlights

Binbin Xu has worked with notable companies such as Beijing Boe Sensor Technology Co., Ltd. and Boe Technology Group Co., Ltd. His experience in these organizations has contributed to his expertise in deep learning and artificial intelligence.

Collaborations

Some of Xu's coworkers include Shuai Xu and Bin Zhao. Their collaboration has likely fostered an environment of innovation and creativity in their respective fields.

Conclusion

Binbin Xu stands out as a key figure in the advancement of deep learning technology. His patents reflect a commitment to improving resource scheduling and model adaptability, which are crucial for the future of artificial intelligence. His work continues to influence the landscape of computer technology.

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